Search Results (67)

This study explores how small and micro fashion and apparel enterprises (SMFAEs) conceptualize and structure social and environmental responsibility within an industry characterized by fragmented supply chains and limited institutional guidance. A qualitative, exploratory case study design examined four Québec-based enterprises through semi-structured interviews; these were analyzed using a hybrid thematic approach interpreted through stakeholder and legitimacy theories. The findings reveal three interdependent dimensions of responsible entrepreneurship: foundational commitments rooted in personal values; organizing mechanisms combining formal tools and informal learning to support continuous improvement; and contextual constraints related to sourcing and systemic opacity. The study advances understanding of early-stage responsibilization as a dynamic alignment between conviction, method, and feasibility. It contributes an integrative model that reframes sustainability from a compliance-oriented goal to an adaptive practice grounded in dialogue and learning. This perspective shows that meaningful sustainability emerges not from universal standards alone but from strengthening everyday human-scale processes of collaboration and adaptation. Full article

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R² of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market. Full article

The digitalization of business activities is already a reality in most developed countries. India, driven by strong information technology, is rapidly digitalizing across business industries. In the retail industry, this shift is visible in the adoption of omnichannel strategies to enhance value for customers, loyalty and trust, retailer’s image, overall shopping experience, and operational productivity. The present study assesses the extent of omnichannel transformation in the five leading Indian retail sectors viz. Consumer Electronics, Fashion and Apparel, Furniture and Home Decor, Grocery and Supermarkets, and Personal Care and Hygiene. The research design uses mystery shopping approach to collect data from 166 nationally present retailers to analyze their level of omnichannel implementation and digital adoption. The authors performed all statistical analyses and visualizations in R using the ggstatsplot package. The results highlight a less-than-ideal picture of channel integration, suggesting that while top retailers in each sector dominate channel integration, most others have made limited progress. Among the sectors, the Furniture and Home Decor sector leads in channel integration. Other contributions of this study include the enhancement of the existing measuring tool by introducing new indicators. The study reveals gaps in omnichannel implementation to help managers plan strategic improvements. Full article

This study investigates the impact of social media marketing activities on customers’ behavioral engagement with fashion clothing brands in Pakistan. Drawing on the Stimulus–Organism–Response (S–O–R) framework, this study examined how social media marketing activities affect brand love and subsequent consumers’ behavioral engagement, and to what extent gender moderates the association between social media marketing activities and brand love. Data were collected through a cross-sectional survey using convenience sampling, resulting in 297 valid responses. Partial Least Squares Structural Equation Modeling using SmartPLS 4 software was employed for the data analysis. The findings revealed that social media marketing activities have a substantial impact on brand love, and brand love, in turn, influences customers’ behavioral engagement. Specifically, social media marketing dimensions, such as entertainment, trendiness, customization, and word of mouth, emerged as statistically significant drivers of brand love, whereas interaction did not exhibit any effects. Furthermore, brand love also serves as a predictor of behavioral engagement. Regarding the moderating role of gender, the results confirm that the effect of customization on brand love is higher for females than for males. Similarly, gender acts as a moderator, indicating that the effect of word of mouth on brand love is stronger for males than for females. This study contributes to the literature by emphasizing the significance of brand love in formulating strategies to enhance consumer engagement in the digital landscape. Full article

The global secondhand apparel industry, valued at USD 256B in 2025, is expanding rapidly. The growing acceptance of secondhand fashion and advancements in retail technology have driven millions of individuals to resell, yet little research has analyzed the motivations behind these decisions. Guided by Consumption Values Theory and Goal-Framing Theory, this qualitative study uses ten in-depth interviews with experienced resellers to examine why individuals participate in apparel reselling. Analysis of the participants’ narratives indicates that financial gain is the dominant driver of participation, followed by the convenience provided by reselling platforms and channels, emotional satisfaction, and contributing to sustainability. Conceptually, the study integrates value-based and goal-based lenses to offer an extensive explanation of reseller motivations, shifting focus from the buyer perspective that has dominated prior research. Practically, the findings suggest that resale platforms can encourage participation by reducing visible fees, enabling faster payout, and simplifying the reselling process, while also making community and environmental benefits more visible. In all, these insights help retailers and sustainability advocates better design approaches that support individual resellers and sustain growth in apparel resale. Full article

The apparel industry is changing dynamically and quickly to manufacturing sustainable fashion products and the development of sustainable design strategies that minimize material consumption at the source. This study addresses a critical research gap by quantitatively evaluating the impact of fusing traditional South Asian garment construction (the kameez) with varied, Western-inspired sleeve geometries on key manufacturing metrics. Thirty-three distinct women’s garment styles, comprising three kameez types (simple, princess-cut, open-front) each paired with eleven different sleeve designs, were developed in the apparel industry to study the effect of fabric efficiency, wastage, and cost-effectiveness. The virtual patterns and markers were drafted and accomplished through Garment Gerber Technology (GGT) software to analyze fabric consumption, fabric efficiency, and cost-effectiveness. The results revealed that paneled kameez styles, such as the princess-cut and open-front, are significantly more material-efficient, achieving average fabric efficiencies of up to 83.95%, compared to the monolithic simple kameez, which averaged only 75.68%. Among sleeve types, multi-constructions like the slit sleeve and cuff sleeve proved most efficient (achieving up to 86.91% efficiency), while voluminous, single-piece designs like the umbrella sleeve consumed the most fabric and were the least efficient. Open-front kameez slit sleeves (OFSL3), simple kameez slit sleeves (SSL3), and princess-cut kameez slit sleeves (PCSL3), were better and more sustainable selections since they were most efficient in fabric efficiency (i.e., 86.91%, 86.17%, and 86.09%). Furthermore, the simple kameez style has the highest fabric wastage (above 22%), while the princess kameez style has the least (below 19%). The simple kameez slit sleeves design (SSL3) has the minimum wastage, while the simple kameez umbrella sleeves design (SSL4) has the maximum wastage. From a cost perspective, the open-front kameez slit sleeve (OFSL1) was identified as the most economical design, whereas the simple kameez with an umbrella sleeve (SSL4) was the most expensive. Statistical analysis confirmed that the differences between kameez styles were significant (p < 0.05). Thus, adoption of specific, sustainable, deliberate design choices and incorporating paneling into the garment body and utilizing multi-piece sleeve constructions offer a quantifiable and strategic approach for manufacturers to reduce material waste, optimize fabric utilization, and improve production cost-effectiveness. Full article

With the growing adoption of CLO 3D in the fashion industry and educational settings, the need for accurate material representation and fit simulation in virtual environments is increasing. This study aimed to evaluate whether CLO 3D, without the aid of physical samples, can reliably simulate clothing pressure for compression wear made from different materials. Unlike previous CLO 3D studies that focused on design or pattern accuracy, this study critically examined material-specific simulation limitations and proposed technical enhancements. Two types of leggings with varying spandex content were tested across five yoga poses using the CLO 3D software(version 2024.2.214). The results showed that CLO 3D did not detect differences in clothing pressure caused by variations in spandex content. Furthermore, the pressure values remained constant across different poses for both fabrics, failing to reflect realistic mechanical differences. The highest total clothing pressure was recorded in the Lunge pose (277.02 kPa), and the lowest in the Plow pose (241.37 kPa). These findings suggest that the current simulation engine lacks sensitivity to fabric-specific mechanical properties and movement-based variation. To address these limitations, this study proposes five optimization functions for CLO 3D, including material property input, technical textile databases, environmental condition settings, AI-based comfort prediction, and data management tools. These proposals are expected to strengthen the scientific validity, functional realism, and user-centered applicability of CLO 3D in designing sportswear, medical compression garments, and customized apparel. Full article

The fashion industry is facing increasing challenges related to textile waste and environmental pollution, driving the need for sustainable material innovations. Bacterial cellulose (BC), a biodegradable and non-polluting biomaterial, has emerged as a promising alternative for the sustainable transformation of fashion materials. Investigations into printing and dyeing techniques are expected to provide methodological frameworks for the design and functional application of BC materials, promoting their adoption and development in the fashion sector. This study, using the kombucha culture method, systematically investigated the cultivation, purification, plasticization, and drying processes of BC as a fashion material, examined its color characteristics using plant and reactive dyeing, and evaluated the effects of pattern printing and the feasibility of traditional plant pigment stencil printing, digital printing, and cyanotype printing on BC. Based on these printing and dyeing methods, digital printing combined with reactive dyeing—offering richer print effects, a wider color gamut, and higher rubbing fastness—was selected to realize the fashion design series Photosynthesis using BC as the primary material. This research contributes methodological insights into the integration of bio-based materials in fashion design and promotes the advancement of sustainable practices within the textile and apparel industries. Full article

This study focuses on the shift to a circular economy (CE) in the textile, apparel, and fashion (TAF) sectors, which generate tons of waste annually. Thus, embracing CE practices is essential for contributing to UN Sustainable Development Goals. This study employs a mixed-methods approach, integrating PRISMA for systematic literature selection, BERTopic modeling and AI-driven SDG mapping, and case study analysis to explore emerging CE themes, implemented circular practices, and systemic barriers. Machine-learning-based SDG mapping reveals strong linkages to SDG 9 and SDG 12, emphasizing technological advancements, industrial collaborations, and circular business models. Moderately explored SDGs, namely, SDG 8, SDG 6, and SDG 7, highlight research on labor conditions, water conservation, and clean energy integration. Reviewing 655 peer-reviewed papers, the BERTopic modeling extracted six key themes, including sustainable recycling, circular business models, and consumer engagement, whereas case studies highlighted regulatory frameworks, stakeholder collaboration, and financial incentives as critical enablers. The findings advance institutional theory by demonstrating how certifications, material standards, and regulations drive CE adoption, reinforcing SDG 12 and SDG 16. The natural resource-based view is extended by showing that technological resources alone are insufficiently aligned with SDG 9. Using the Antecedents–Decisions–Outcomes framework, this study presents a structured, AI-driven roadmap for scaling CE in the TAF industry, addressing systemic barriers, and supporting global sustainability goals, highlighting how multistakeholder collaboration, digital traceability, and inclusive governance can improve the impact of CE. The results recommend that CE strategies should be aligned with net-zero targets, carbon credit systems, and block-chain-based supply chains. Full article

A significant proportion of waste generated by the fashion industry is either landfilled or incinerated, primarily due to the high cost and complexity of collecting and separating mixed textile materials. While research in textile recycling often emphasizes post-consumer waste, less attention is given to pre-consumer waste, particularly cutting surpluses generated during apparel manufacturing—a labour-intensive sector with low automation and operational inefficiencies. This study addresses this gap by presenting a case study on the implementation of an automated system for collecting, transporting, sorting, and storing textile surpluses in an apparel manufacturing environment. The research aims to identify the barriers, benefits, and sustainability impact of such automation. Using both qualitative and quantitative data, the system is evaluated through key performance indicators including time reduction, ergonomic improvement, and process reliability. Results suggest that automation enhances intralogistics, reduces non-value-added labour, and enables better utilization of human resources. This case study offers a practical framework for apparel manufacturers to assess the potential of automating textile-waste handling, helping to justify such investments based on labour use, process variability, and environmental benefits. The study contributes to the broader discourse on sustainable manufacturing and supports the apparel industry’s shift toward digital transformation and circular economy practices. Full article

In recent years, the fashion industry has undergone a significant transformation driven by digital innovations, particularly with the emergence of the digital retail theater (DRT). A DRT integrates augmented reality (AR), virtual reality (VR), and 3D modeling to create immersive shopping experiences that bridge the physical and digital worlds. This study specifically focuses on apparel DRTs and investigates the key factors influencing U.S. consumers’ intention to adopt this technology. Drawing on the unified theory of acceptance and use of technology (UTAUT) and perceived risk theory, we developed and tested an integrative research model. Primary data were collected through a structured online survey administered via Amazon Mechanical Turk (MTurk). A total of 400 valid responses were obtained from U.S. consumers. Data were analyzed using multiple regression analysis to examine the hypothesized relationships. The results indicate that effort expectancy (ease of use), facilitating conditions (technical infrastructure), physical risk (concerns about potential harm), and time/convenience loss risk significantly influence consumers’ intention to adopt apparel DRTs. Surprisingly, performance expectancy and social influence were not significant predictors of DRT adoption. These findings provide valuable insights for apparel retailers, emphasizing the importance of user-friendly designs, robust technical infrastructure, and minimizing perceived risks to foster DRT adoption. Full article

Greenwashing, the phenomenon of companies misleading consumers about their sustainability practices, is prevalent in the fashion industry. This study explores consumer opinions on greenwashing through analysis of social media discourse. Cognitive dissonance theory served as the theoretical framework, explaining how consumers reconcile conflicting information about brands’ sustainability claims. In Study 1, 446 comments on 12 Reddit posts were collected using the search term “fashion greenwashing”. Using the Latent Dirichlet Allocation (LDA) algorithm and manual review, we identified three major themes: the phenomenon of fashion greenwashing, consumer empowerment in sustainable fashion, and skepticism towards fast fashion brands’ marketing strategies. In Study 2, using the search term, “#fashiongreenwashing”, two researchers collected and analyzed 76 Instagram posts with 370 comments. A manual review was employed to extract major themes, and network graphs of caption tags within the same theme were constructed. Three major themes emerged: strategies to combat fashion greenwashing, examples of fashion greenwashing, and advocacy and regulation in sustainable fashion. Findings from Studies 1 and 2 revealed that consumers are increasingly aware of brands’ deceptive practices and advocacy for sustainable practices to resolve this dissonance when they see greenwashing information. This study underscored the need for fashion brands to provide transparent and authentic information. Full article

Within the global wave of manufacturing intelligence, AI technologies are revolutionizing industrial frameworks through deep integration. As a resource-intensive sector, fashion has become a pivotal arena for assessing AI’s role in sustainable development. China, the world’s largest apparel producer, faces unique AI integration challenges, highlighting the intersection of innovation and sustainability. To further explore the impact of AI-powered try-on technology on the impulsive buying intentions of young Chinese consumers, this research utilizes a modified version of the stimulus–organism–response (SOR) model. From the lens of online shopping, the research investigates how key features of AI-powered try-on technology, such as visual vividness, interactive control, personalized configuration, and ease of use, affect impulsive buying intentions. Additionally, the study examines the mediating roles of perceived utilitarian value, perceived hedonic value, and perceived immersion, alongside the moderating role of brand trust. A structured online survey was conducted with 366 participants, and the data were analyzed using the partial least squares (PLS) method. The findings reveal that the four core attributes of AI-powered try-on technology have a positive effect on impulsive buying intentions. Furthermore, the mediating roles of perceived utilitarian value, perceived hedonic value, and perceived immersion, along with the moderating influence of brand trust, were substantiated. In the realm of online apparel shopping, AI-powered try-on technology effectively stimulates impulsive buying behavior and drives online purchases. These results offer valuable theoretical insights for enhancing AI-powered try-on applications, while also providing strategic guidance for fashion brands and e-commerce platforms in developing AI-driven sustainable marketing approaches. Full article

In the context of new retail, the development of omni-channels is flourishing. The entry threshold for the clothing industry is low, and the popularity of online shopping has, to some extent, reduced consumers’ perception of the authenticity of clothing. As a result, returns are a serious issue in the clothing industry. This article focuses on a clothing retailer while addressing retail and return issues in the clothing industry. It develops and analyzes models for an online single-channel strategy and two omni-channel showroom strategies: “Experience in Store and Buy Online (ESBO)” with an experience store and “Buy Online and Return in Store (BORS)” with a physical store. These models are used to examine the pricing and return decisions of the retailer in the three strategic scenarios. Additionally, this study considers the impact of fashion trends on demand. It explores pricing and return strategies in two showroom models under the influence of the fashion trend decay factor. Moreover, sensitivity analyses and numerical analyses of the important parameters are performed. This research demonstrates the following: (1) In the case of high return transportation costs and online return hassle costs, clothing retailers can attract consumers to increase profits through establishing offline channels; (2) extending the sales time of fashionable clothing has a positive effect on profits, but blindly prolonging the continuation of the sales time will lead to a decrease in profits; (3) the larger the initial fashion level or the smaller the fashion level decay factor, the greater the optimal retailer profits. The impacts of the initial fashion level and fashion level decay factor on profits are more significant in omni-channel operations. This article aims to identify optimal strategies for retailers utilizing omni-channel operations and offer managerial insights for the sale of fashionable apparel. Full article

The fashion industry has a detrimental environmental impact throughout its supply chain operations that needs immediate attention. Limited work focuses on measuring the environmental sustainability of clothing products made from post-consumer waste in the circular economy. This research aims to evaluate the environmental sustainability of fashion products made from post-consumer waste using the Higg Index tool developed by the Sustainable Apparel Coalition. Three t-shirt manufacturers—namely factory A (LEED certified), factory B (non-LEED certified), and factory C (Sub-contract) were considered as case studies. Data were collected through practice-based qualitative questions to manufacturing practitioners, which were supplemented by triangulations, and scores were obtained using the Higg Index product environmental sustainability tool. The findings highlight significant variations in most subsection analyses for product environment sustainability dimensions, scoring 369.5 (73.9%), 277.6 (55.5%), and 153.5 (30.7%) out of 500 by factories A, B, and C, respectively. Findings reveal significant differences in scores (from low to high) for the three t-shirt manufacturers. Various subsections revealed deficiencies in actual policies including product design, materials selection, manufacturing operations, and priorities regarding subcategories. Products made in a green factory and embracing the circular economy achieved the highest score, while the sub-contractor factory product obtained the lowest score. Findings highlight poor and emerging sustainable practices, identify challenges, and suggest improvement in the above-mentioned categories. The research urges stakeholders to embrace sustainable practices for fashion products to reduce environmental impact through life cycle stages and benefit the industry. Full article